Enhanced YOLOv8 with lightweight and efficient detection head for for detecting rice leaf diseases.

Abstract

Detecting rice leaf diseases is essential for agricultural stability and crop health. However, the diversity of these diseases, their uneven distribution, and complex field environments create challenges for precise, multi-scale detection. While YOLO object detection algorithms show strong performance in automated detection, their feature extraction capabilities remain limited in complex agricultural settings. Moreover, their high computational demands hinder deployment on resource-constrained devices, necessitating further optimization.To overcome these issues, This paper presents G-YOLO, a novel architecture that combines a Lightweight and Efficient Detection Head (LEDH) with Multi-scale Spatial Pyramid Pooling Fast (MSPPF). The LEDH enhances detection speed by simplifying the network structure while maintaining accuracy, reducing computational demands. The MSPPF improves the model's ability to capture intricate details of rice leaf diseases at various scales by fusing multi-level feature maps. On the RiceDisease dataset, G-YOLO surpasses YOLOv8n with 4.4% higher mAP@0.5, 3.9% higher mAP@0.75, and a 13.1% increase in FPS, making it well-suited for resource-constrained devices due to its efficient design.

Plant phenotyping relevance

イネ葉の病害状態を画像から検出する新規YOLOアーキテクチャを開発し、データセット上で精度・速度を評価しており、病害フェノタイピング手法が中心である。

Code and data availability

The paper uses the RiceDisease dataset and a custom G-YOLO model, but provides no public repository for the authors' code, trained weights, or annotations. The Data availability statement explicitly restricts access: all data are available only through the corresponding author. The Kaggle RiceDisease dataset is a cited

No evidence-backed public reproduction asset is currently recorded.